Two-phase snapshot recovery

ABSTRACT

In some examples, a data management and storage (DMS) platform comprises peer DMS nodes in a node cluster, a distributed data store comprising local and cloud storage, and at least one processor configured to perform operations in a method of creating a local consolidated patch file from a patch file chain stored in the cloud storage. Example operations comprise, in a first dry-run phase, creating a patch file image of data blocks in one or more cloud patch files stored in the cloud storage; in a second data-transfer phase, downloading at least some of the data blocks from the cloud patch files identified by the patch file image; and creating and storing, in the local storage, the local consolidated patch file using the downloaded data blocks.

FIELD

The present disclosure relates generally to computer architecturesoftware for a data management platform. Some examples relate generallyto methods and systems for two-phase snapshot recovery. Some examplesseek to provide two-phase snapshot recovery from the cloud with smartprefetch and minimal redundant-download features.

BACKGROUND

The volume and complexity of data that is collected, analyzed, andstored is increasing rapidly over time. The computer infrastructure usedto handle this data is also becoming more complex, with more processingpower and more portability. As a result, data management and storage isbecoming increasingly important. Significant issues of these processesinclude access to reliable data backup and storage and fast datarecovery in cases of failure. Other aspects include data portabilityacross locations and platforms.

Virtual machines (VM's) that include virtual disks are sometimes backedup by taking snapshots. In a snapshot-based approach, a base snapshot istaken when a protection policy under a service level agreement (SLA) forexample is enabled on a VM and its virtual disks. After the basesnapshot is saved on a backup site, incremental snapshots are takenperiodically. A delta between two snapshots represents data blocks thathave changed, and these blocks may be sent to and stored on a backupsite for recovery when needed. Since taking snapshots is an expensiveoperation and may impact users, snapshots are typically taken someminutes apart, often from the tens of minutes to several hours, and thisin turn can result in very poor recovery point objectives (RPOs).

The background description provided herein is to generally present thecontext of the disclosure. It should be noted that the informationdescribed in this section is presented to provide the skilled artisansome context for the following disclosed subject matter and should notbe considered as admitted prior art. More specifically, work of thepresently named inventors, to the extent it is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

SUMMARY

Some general examples disclose a two-phase approach for downloading asnapshot from an off-site data facility or backup site (e.g. the cloud)that seeks to enable maximum throughput with minimum cost by performinglarge reads from the cloud in parallel. To this end, in a first phase(also known as a “dry-run” phase), some examples build a profile thatdefines precisely which parts of which source patch file (describedfurther below) should be read, and in what specific order, for a “mosteffective” download. In a second phase (also known as a data-transferphase), some examples use this profile to coalesce smaller reads intolarger ones and read them from the cloud in parallel. During the secondphase, some examples know ahead of time exactly what objects to readwhich helps a quick read limited to exactly what data is needed for agiven snapshot download. That is, some examples do not read any datathat would only be discarded later, thereby reducing cost and improvingRPOs for system administrators and users.

In some examples, a data management and storage (DMS) platform comprisespeer DMS nodes in a node cluster; a distributed data store comprisinglocal and cloud storage; and at least one processor configured toperform operations in a method of creating a local consolidated patchfile from a patch file chain stored in the cloud storage, the operationscomprising: in a first dry-run phase, creating a patch file image ofdata blocks in one or more cloud patch files stored in the cloudstorage; in a second data-transfer phase, downloading at least some ofthe data blocks from the cloud patch files identified by the patch fileimage; and creating and storing, in the local storage, the localconsolidated patch file using the downloaded data blocks.

In some examples, the operations further comprise performing a snapshotrecovery using the local consolidated patch file.

In some examples, the first dry-run phase further comprises scanningindex blocks in the one or more cloud patch files to identify datablocks for downloading in the second data-transfer phase, the patch fileimage based on the scanning of the index blocks.

In some examples, the patch file image includes one or more tuples, eachtuple including values for or information concerning a cloud patch filepath, a cloud patch file offset, and a cloud patch file size.

In some examples, the second data-transfer phase further comprisesscanning the patch image file created in the first dry-run phase anddownloading at least some of the data blocks identified for downloadingsimultaneously.

In some examples, the second data-transfer phase comprise a coalescingoperation to construct larger reads than would occur when read inparallel.

In some examples, a file size of the cloud patch file is in the range200-300 GB, and a file size of the patch file image is in the range30-40 MB.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe views of the accompanying drawing:

FIG. 1 depicts one embodiment of a networked computing environment inwhich the disclosed technology may be practiced, according to an exampleembodiment.

FIG. 2 depicts one embodiment of the server of FIG. 1, according to anexample embodiment.

FIG. 3 depicts one embodiment of the storage appliance of FIG. 1,according to an example embodiment.

FIG. 4 shows an example cluster of a distributed decentralized database,according to some example embodiments.

FIG. 5 shows how an example patch file is laid out on a disk, accordingto an example embodiment.

FIG. 6 shows an example download mechanism, according to an exampleembodiment.

FIG. 7 shows a patch file image, according to an example embodiment.

FIG. 8 shows aspects of a two-phase method of snapshot recovery,according to example embodiments.

FIG. 9 depicts a block flow chart indicating example operations in amethod, according to example embodiments.

FIG. 10 depicts a block flow chart indicating example operations in amethod, according to example embodiments.

FIG. 11 depicts a block diagram illustrating an example of a softwarearchitecture that may be installed on a machine, according to someexample embodiments.

FIG. 12 depicts a block diagram illustrating an architecture ofsoftware, according to an example embodiment

FIG. 13 illustrates a diagrammatic representation of a machine 1000 inthe form of a computer system within which a set of instructions may beexecuted for causing a machine to perform any one or more of themethodologies discussed herein, according to an example embodiment.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the present disclosure. In thefollowing description, for purposes of explanation, numerous specificdetails are set forth in order to provide a thorough understanding ofexample embodiments. It will be evident, however, to one skilled in theart that the present inventive subject matter may be practiced withoutthese specific details. In this disclosure, the term “snappable” refersto a data object or file that is capable of being copied or backed up,or of which a snapshot can be taken.

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in the drawings that form a part of thisdocument: Copyright Rubrik, Inc., 2020-2021, All Rights Reserved.

It will be appreciated that some of the examples disclosed herein aredescribed in the context of virtual machines that are backed up by usingbase and incremental snapshots, for example. This should not necessarilybe regarded as limiting of the disclosures. The disclosures, systems,and methods described herein apply not only to virtual machines of alltypes that run a file system (for example), but also to Network AttachedStorage (NAS) devices, physical machines (for example Linux servers),and databases.

FIG. 1 depicts one embodiment of a networked computing environment 100in which the disclosed technology may be practiced. As depicted, thenetworked computing environment 100 includes a data center 106, astorage appliance 102, and a computing device 108 in communication witheach other via one or more networks 128. The networked computingenvironment 100 may also include a plurality of computing devicesinterconnected through one or more networks 128. The one or morenetworks 128 may allow computing devices and/or storage devices toconnect to and communicate with other computing devices and/or otherstorage devices. In some cases, the networked computing environment 100may include other computing devices and/or other storage devices notshown. The other computing devices may include, for example, a mobilecomputing device, a non-mobile computing device, a server, aworkstation, a laptop computer, a tablet computer, a desktop computer,or an information processing system. The other storage devices mayinclude, for example, a storage area network storage device, anetworked-attached storage device, a hard disk drive, a solid-statedrive, or a data storage system.

The data center 106 may include one or more servers, such as server 200,in communication with one or more storage devices, such as storagedevice 104. The one or more servers may also be in communication withone or more storage appliances, such as storage appliance 102. Theserver 200, storage device 104, and storage appliance 300 may be incommunication with each other via a networking fabric connecting serversand data storage units within the data center 106 to each other. Thestorage appliance 300 may include a data management system for backingup virtual machines and/or files within a virtualized infrastructure.The server 200 may be used to create and manage one or more virtualmachines associated with a virtualized infrastructure.

The one or more virtual machines may run various applications, such as adatabase application or a web server. The storage device 104 may includeone or more hardware storage devices for storing data, such as a harddisk drive (HDD), a magnetic tape drive, a solid-state drive (SSD), astorage area network (SAN) storage device, or a NAS device. In somecases, a data center, such as data center 106, may include thousands ofservers and/or data storage devices in communication with each other.The one or more data storage devices 104 may comprise a tiered datastorage infrastructure (or a portion of a tiered data storageinfrastructure). The tiered data storage infrastructure may allow forthe movement of data across different tiers of a data storageinfrastructure between higher-cost, higher-performance storage devices(e.g., solid-state drives and hard disk drives) and relativelylower-cost, lower-performance storage devices (e.g., magnetic tapedrives).

The one or more networks 128 may include a secure network such as anenterprise private network, an unsecure network such as a wireless opennetwork, a local area network (LAN), a wide area network (WAN), and theInternet. The one or more networks 128 may include a cellular network, amobile network, a wireless network, or a wired network. Each network ofthe one or more networks 128 may include hubs, bridges, routers,switches, and wired transmission media such as a direct-wiredconnection. The one or more networks 128 may include an extranet orother private network for securely sharing information or providingcontrolled access to applications or files.

A server, such as server 200, may allow a client to download informationor files (e.g., executable, text, application, audio, image, or videofiles) from the server 200 or to perform a search query related toparticular information stored on the server 200. In some cases, a servermay act as an application server or a file server. In general, server200 may refer to a hardware device that acts as the host in aclient-server relationship or a software process that shares a resourcewith or performs work for one or more clients.

One embodiment of server 200 includes a network interface 110, processor112, memory 114, disk 116, and virtualization manager 118 all incommunication with each other. Network interface 110 allows server 200to connect to one or more networks 128. Network interface 110 mayinclude a wireless network interface and/or a wired network interface.Processor 112 allows server 200 to execute computer-readableinstructions stored in memory 114 in order to perform processesdescribed herein. Processor 112 may include one or more processingunits, such as one or more CPUs and/or one or more GPUs. Memory 114 maycomprise one or more types of memory (e.g., RAM, SRAM, DRAM, ROM,EEPROM, Flash, etc.). Disk 116 may include a hard disk drive and/or asolid-state drive. Memory 114 and disk 116 may comprise hardware storagedevices.

The virtualization manager 118 may manage a virtualized infrastructureand perform management operations associated with the virtualizedinfrastructure. The virtualization manager 118 may manage theprovisioning of virtual machines running within the virtualizedinfrastructure and provide an interface to computing devices interactingwith the virtualized infrastructure. In one example, the virtualizationmanager 118 may set a virtual machine having a virtual disk into afrozen state in response to a snapshot request made via an applicationprogramming interface (API) by a storage appliance, such as storageappliance 300. Setting the virtual machine into a frozen state may allowa point in time snapshot of the virtual machine to be stored ortransferred. In one example, updates made to a virtual machine that hasbeen set into a frozen state may be written to a separate file (e.g., anupdate file) while the virtual disk may be set into a read-only state toprevent modifications to the virtual disk file while the virtual machineis in the frozen state.

The virtualization manager 118 may then transfer data associated withthe virtual machine (e.g., an image of the virtual machine or a portionof the image of the virtual disk file associated with the state of thevirtual disk at the point in time it is frozen) to a storage appliance(for example, a storage appliance 102 or storage appliance 300 of FIG.1, described further below) in response to a request made by the storageappliance. After the data associated with the point in time snapshot ofthe virtual machine has been transferred to the storage appliance 300(for example), the virtual machine may be released from the frozen state(i.e., unfrozen) and the updates made to the virtual machine and storedin the separate file may be merged into the virtual disk file. Thevirtualization manager 118 may perform various virtual machine-relatedtasks, such as cloning virtual machines, creating new virtual machines,monitoring the state of virtual machines, moving virtual machinesbetween physical hosts for load balancing purposes, and facilitatingbackups of virtual machines.

One embodiment of a storage appliance 300 (or storage appliance 102)includes a network interface 120, processor 122, memory 124, and disk126 all in communication with each other. Network interface 120 allowsstorage appliance 300 to connect to one or more networks 128. Networkinterface 120 may include a wireless network interface and/or a wirednetwork interface. Processor 122 allows storage appliance 300 to executecomputer readable instructions stored in memory 124 in order to performprocesses described herein. Processor 122 may include one or moreprocessing units, such as one or more CPUs and/or one or more GPUs.Memory 124 may comprise one or more types of memory (e.g., RAM, SRAM,DRAM, ROM, EEPROM, NOR Flash, NAND Flash, etc.). Disk 126 may include ahard disk drive and/or a solid-state drive. Memory 124 and disk 126 maycomprise hardware storage devices.

In one embodiment, the storage appliance 300 may include four machines.Each of the four machines may include a multi-core CPU, 64 GB of RAM, a400 GB SSD, three 4 TB HDDs, and a network interface controller. In thiscase, the four machines may be in communication with the one or morenetworks 128 via the four network interface controllers. The fourmachines may comprise four nodes of a server cluster. The server clustermay comprise a set of physical machines that are connected together viaa network. The server cluster may be used for storing data associatedwith a plurality of virtual machines, such as backup data associatedwith different point-in-time versions of the virtual machines.

The networked computing environment 100 may provide a cloud computingenvironment for one or more computing devices. Cloud computing may referto Internet-based computing, wherein shared resources, software, and/orinformation may be provided to one or more computing devices on-demandvia the Internet. The networked computing environment 100 may comprise acloud computing environment providing Software-as-a-Service (SaaS) orInfrastructure-as-a-Service (IaaS) services. SaaS may refer to asoftware distribution model in which applications are hosted by aservice provider and made available to end users over the Internet. Inone embodiment, the networked computing environment 100 may include avirtualized infrastructure that provides software, data processing,and/or data storage services to end users accessing the services via thenetworked computing environment 100. In one example, networked computingenvironment 100 may provide cloud-based work productivity orbusiness-related applications to a computing device, such as computingdevice 108. The storage appliance 102 may comprise a cloud-based datamanagement system for backing up virtual machines and/or files within avirtualized infrastructure, such as virtual machines running on server200/or files stored on server 200.

In some cases, networked computing environment 100 may provide remoteaccess to secure applications and files stored within data center 106from a remote computing device, such as computing device 108. The datacenter 106 may use an access control application to manage remote accessto protected resources, such as protected applications, databases, orfiles located within the data center 106. To facilitate remote access tosecure applications and files, a secure network connection may beestablished using a virtual private network (VPN). A VPN connection mayallow a remote computing device, such as computing device 108, tosecurely access data from a private network (e.g., from a company fileserver or mail server) using an unsecure public network or the Internet.The VPN connection may require client-side software (e.g., running onthe remote computing device) to establish and maintain the VPNconnection. The VPN client software may provide data encryption andencapsulation prior to the transmission of secure private networktraffic through the Internet.

In some embodiments, the storage appliance 300 may manage the extractionand storage of virtual machine snapshots associated with different pointin time versions of one or more virtual machines running within the datacenter 106. A snapshot of a virtual machine may correspond with a stateof the virtual machine at a particular point-in-time. In response to arestore command from the storage device 104, the storage appliance 300may restore a point-in-time version of a virtual machine or restorepoint-in-time versions of one or more files located on the virtualmachine and transmit the restored data to the server 200. In response toa mount command from the server 200, the storage appliance 300 may allowa point-in-time version of a virtual machine to be mounted and allow theserver 200 to read and/or modify data associated with the point-in-timeversion of the virtual machine. To improve storage density, the storageappliance 300 may deduplicate and compress data associated withdifferent versions of a virtual machine and/or deduplicate and compressdata associated with different virtual machines. To improve systemperformance, the storage appliance 300 may first store virtual machinesnapshots received from a virtualized environment in a cache, such as aflash-based cache. The cache may also store popular data or frequentlyaccessed data (e.g., based on a history of virtual machine restorations,incremental files associated with commonly restored virtual machineversions) and current day incremental files or incremental filescorresponding with snapshots captured within the past 24 hours.

An incremental file may comprise a forward incremental file or a reverseincremental file. A forward incremental file may include a set of datarepresenting changes that have occurred since an earlier point-in-timesnapshot of a virtual machine. To generate a snapshot of the virtualmachine corresponding with a forward incremental file, the forwardincremental file may be combined with an earlier point in time snapshotof the virtual machine (e.g., the forward incremental file may becombined with the last full image of the virtual machine that wascaptured before the forward incremental file was captured and any otherforward incremental files that were captured subsequent to the last fullimage and prior to the forward incremental file). A reverse incrementalfile may include a set of data representing changes from a laterpoint-in-time snapshot of a virtual machine. To generate a snapshot ofthe virtual machine corresponding with a reverse incremental file, thereverse incremental file may be combined with a later point-in-timesnapshot of the virtual machine (e.g., the reverse incremental file maybe combined with the most recent snapshot of the virtual machine and anyother reverse incremental files that were captured prior to the mostrecent snapshot and subsequent to the reverse incremental file).

The storage appliance 300 may provide a user interface (e.g., aweb-based interface or a graphical user interface) that displays virtualmachine backup information such as identifications of the virtualmachines protected and the historical versions or time machine views foreach of the virtual machines protected. A time machine view of a virtualmachine may include snapshots of the virtual machine over a plurality ofpoints in time. Each snapshot may comprise the state of the virtualmachine at a particular point in time. Each snapshot may correspond witha different version of the virtual machine (e.g., Version 1 of a virtualmachine may correspond with the state of the virtual machine at a firstpoint in time and Version 2 of the virtual machine may correspond withthe state of the virtual machine at a second point in time subsequent tothe first point in time).

The user interface may enable an end user of the storage appliance 300(e.g., a system administrator or a virtualization administrator) toselect a particular version of a virtual machine to be restored ormounted. When a particular version of a virtual machine has beenmounted, the particular version may be accessed by a client (e.g., avirtual machine, a physical machine, or a computing device) as if theparticular version was local to the client. A mounted version of avirtual machine may correspond with a mount point directory (e.g.,/snapshots/VM5Nersion23). In one example, the storage appliance 300 mayrun an NFS server and make the particular version (or a copy of theparticular version) of the virtual machine accessible for reading and/orwriting. The end user of the storage appliance 300 may then select theparticular version to be mounted and run an application (e.g., a dataanalytics application) using the mounted version of the virtual machine.In another example, the particular version may be mounted as an iSCSItarget.

FIG. 2 depicts one embodiment of server 200 of FIG. 1. The server 200may comprise one server out of a plurality of servers that are networkedtogether within a data center (e.g., data center 106). In one example,the plurality of servers may be positioned within one or more serverracks within the data center. As depicted, the server 200 includeshardware-level components and software-level components. Thehardware-level components include one or more processors 202, one ormore memories 204, and one or more disks 206. The software-levelcomponents include a hypervisor 208, a virtualized infrastructuremanager 222, and one or more virtual machines, such as virtual machine220. The hypervisor 208 may comprise a native hypervisor or a hostedhypervisor. The hypervisor 208 may provide a virtual operating platformfor running one or more virtual machines, such as virtual machine 220.Virtual machine 220 includes a plurality of virtual hardware devicesincluding a virtual processor 210, a virtual memory 212, and a virtualdisk 214. The virtual disk 214 may comprise a file stored within the oneor more disks 206. In one example, a virtual machine 220 may include aplurality of virtual disks 214, with each virtual disk of the pluralityof virtual disks 214 associated with a different file stored on the oneor more disks 206. Virtual machine 220 may include a guest operatingsystem 216 that runs one or more applications, such as application 218.

The virtualized infrastructure manager 222, which may correspond withthe virtualization manager 118 in FIG. 1, may run on a virtual machineor natively on the server 200. The virtual machine may, for example, beor include the virtual machine 220 or a virtual machine separate fromthe server 200. Other arrangements are possible. The virtualizedinfrastructure manager 222 may provide a centralized platform formanaging a virtualized infrastructure that includes a plurality ofvirtual machines. The virtualized infrastructure manager 222 may managethe provisioning of virtual machines running within the virtualizedinfrastructure and provide an interface to computing devices interactingwith the virtualized infrastructure. The virtualized infrastructuremanager 222 may perform various virtualized infrastructure relatedtasks, such as cloning virtual machines, creating new virtual machines,monitoring the state of virtual machines, and facilitating backups ofvirtual machines.

In one embodiment, the server 200 may use the virtualized infrastructuremanager 222 to facilitate backups for a plurality of virtual machines(e.g., eight different virtual machines) running on the server 200. Eachvirtual machine running on the server 200 may run its own guestoperating system and its own set of applications. Each virtual machinerunning on the server 200 may store its own set of files using one ormore virtual disks associated with the virtual machine (e.g., eachvirtual machine may include two virtual disks that are used for storingdata associated with the virtual machine).

In one embodiment, a data management application running on a storageappliance, such as storage appliance 102 in FIG. 1 or storage appliance300 in FIG. 1, may request a snapshot of a virtual machine running onserver 200. The snapshot of the virtual machine may be stored as one ormore files, with each file associated with a virtual disk of the virtualmachine. A snapshot of a virtual machine may correspond with a state ofthe virtual machine at a particular point in time. The particular pointin time may be associated with a time stamp. In one example, a firstsnapshot of a virtual machine may correspond with a first state of thevirtual machine (including the state of applications and files stored onthe virtual machine) at a first point in time, and a second snapshot ofthe virtual machine may correspond with a second state of the virtualmachine at a second point in time subsequent to the first point in time.

In response to a request for a snapshot of a virtual machine at aparticular point in time, the virtualized infrastructure manager 222 mayset the virtual machine into a frozen state or store a copy of thevirtual machine at the particular point in time. The virtualizedinfrastructure manager 222 may then transfer data associated with thevirtual machine (e.g., an image of the virtual machine or a portion ofthe image of the virtual machine) to the storage appliance 300 orstorage appliance 102. The data associated with the virtual machine mayinclude a set of files including a virtual disk file storing contents ofa virtual disk of the virtual machine at the particular point in timeand a virtual machine configuration file storing configuration settingsfor the virtual machine at the particular point in time. The contents ofthe virtual disk file may include the operating system used by thevirtual machine, local applications stored on the virtual disk, and userfiles (e.g., images and word processing documents). In some cases, thevirtualized infrastructure manager 222 may transfer a full image of thevirtual machine to the storage appliance 102 or storage appliance 300 ofFIG. 1 or a plurality of data blocks corresponding with the full image(e.g., to enable a full image-level backup of the virtual machine to bestored on the storage appliance). In other cases, the virtualizedinfrastructure manager 222 may transfer a portion of an image of thevirtual machine associated with data that has changed since an earlierpoint in time prior to the particular point in time or since a lastsnapshot of the virtual machine was taken. In one example, thevirtualized infrastructure manager 222 may transfer only data associatedwith virtual blocks stored on a virtual disk of the virtual machine thathave changed since the last snapshot of the virtual machine was taken.In one embodiment, the data management application may specify a firstpoint in time and a second point in time and the virtualizedinfrastructure manager 222 may output one or more virtual data blocksassociated with the virtual machine that have been modified between thefirst point in time and the second point in time.

In some embodiments, the server 200 or the hypervisor 208 maycommunicate with a storage appliance, such as storage appliance 102 inFIG. 1 or storage appliance 300 in FIG. 1, using a distributed filesystem protocol such as NFS Version 3, or Server Message Block (SMB)protocol. The distributed file system protocol may allow the server 200or the hypervisor 208 to access, read, write, or modify files stored onthe storage appliance as if the files were locally stored on the server200. The distributed file system protocol may allow the server 200 orthe hypervisor 208 to mount a directory or a portion of a file systemlocated within the storage appliance.

FIG. 3 depicts one embodiment of storage appliance 300 in FIG. 1. Thestorage appliance may include a plurality of physical machines that maybe grouped together and presented as a single computing system. Eachphysical machine of the plurality of physical machines may comprise anode in a cluster (e.g., a failover cluster). In one example, thestorage appliance may be positioned within a server rack within a datacenter. As depicted, the storage appliance 300 includes hardware-levelcomponents and software-level components. The hardware-level componentsinclude one or more physical machines, such as physical machine 314 andphysical machine 324. The physical machine 314 includes a networkinterface 316, processor 318, memory 320, and disk 322 all incommunication with each other. Processor 318 allows physical machine 314to execute computer readable instructions stored in memory 320 toperform processes described herein. Disk 322 may include a hard diskdrive and/or a solid-state drive. The physical machine 324 includes anetwork interface 326, processor 328, memory 330, and disk 332 all incommunication with each other. Processor 328 allows physical machine 324to execute computer readable instructions stored in memory 330 toperform processes described herein. Disk 332 may include a hard diskdrive and/or a solid-state drive. In some cases, disk 332 may include aflash-based SSD or a hybrid HDD/SSD drive. In one embodiment, thestorage appliance 300 may include a plurality of physical machinesarranged in a cluster (e.g., eight machines in a cluster). Each of theplurality of physical machines may include a plurality of multi-coreCPUs, 108 GB of RAM, a 500 GB SSD, four 4 TB HDDs, and a networkinterface controller.

In some embodiments, the plurality of physical machines may be used toimplement a cluster-based network fileserver. The cluster-based networkfile server may neither require nor use a front-end load balancer. Oneissue with using a front-end load balancer to host the IP address forthe cluster-based network file server and to forward requests to thenodes of the cluster-based network file server is that the front-endload balancer comprises a single point of failure for the cluster-basednetwork file server. In some cases, the file system protocol used by aserver, such as server 200 in FIG. 1, or a hypervisor, such ashypervisor 208 in FIG. 2, to communicate with the storage appliance 300may not provide a failover mechanism (e.g., NFS Version 3). In the casethat no failover mechanism is provided on the client side, thehypervisor may not be able to connect to a new node within a cluster inthe event that the node connected to the hypervisor fails.

In some embodiments, each node in a cluster may be connected to eachother via a network and may be associated with one or more IP addresses(e.g., two different IP addresses may be assigned to each node). In oneexample, each node in the cluster may be assigned a permanent IP addressand a floating IP address and may be accessed using either the permanentIP address or the floating IP address. In this case, a hypervisor, suchas hypervisor 208 in FIG. 2, may be configured with a first floating IPaddress associated with a first node in the cluster. The hypervisor mayconnect to the cluster using the first floating IP address. In oneexample, the hypervisor may communicate with the cluster using the NFSVersion 3 protocol. Each node in the cluster may run a Virtual RouterRedundancy Protocol (VRRP) daemon. A daemon may comprise a backgroundprocess. Each VRRP daemon may include a list of all floating IPaddresses available within the cluster. In the event that the first nodeassociated with the first floating IP address fails, one of the VRRPdaemons may automatically assume or pick up the first floating IPaddress if no other VRRP daemon has already assumed the first floatingIP address. Therefore, if the first node in the cluster fails orotherwise goes down, then one of the remaining VRRP daemons running onthe other nodes in the cluster may assume the first floating IP addressthat is used by the hypervisor for communicating with the cluster.

In order to determine which of the other nodes in the cluster willassume the first floating IP address, a VRRP priority may beestablished. In one example, given a number (N) of nodes in a clusterfrom node(0) to node(N−1), for a floating IP address (i), the VRRPpriority of nodeG) may be G-i) modulo N. In another example, given anumber (N) of nodes in a cluster from node(0) to node(N−1), for afloating IP address (i), the VRRP priority of nodeG) may be (i-j) moduloN. In these cases, nodeG) will assume floating IP address (i) only ifits VRRP priority is higher than that of any other node in the clusterthat is alive and announcing itself on the network. Thus, if a nodefails, then there may be a clear priority ordering for determining whichother node in the cluster will take over the failed node's floating IPaddress.

In some cases, a cluster may include a plurality of nodes and each nodeof the plurality of nodes may be assigned a different floating IPaddress. In this case, a first hypervisor may be configured with a firstfloating IP address associated with a first node in the cluster, asecond hypervisor may be configured with a second floating IP addressassociated with a second node in the cluster, and a third hypervisor maybe configured with a third floating IP address associated with a thirdnode in the cluster.

As depicted in FIG. 3, the software-level components of the storageappliance 300 may include data management system 302, a virtualizationinterface 304, a distributed job scheduler 308, a distributed metadatastore 310, a distributed file system 312, and one or more virtualmachine search indexes, such as virtual machine search index 306. In oneembodiment, the software-level components of the storage appliance 300may be run using a dedicated hardware-based appliance. In anotherembodiment, the software-level components of the storage appliance 300may be run from the cloud (e.g., the software-level components may beinstalled on a cloud service provider).

In some cases, the data storage across a plurality of nodes in a cluster(e.g., the data storage available from the one or more physical machine(e.g., physical machine 314 and physical machine 324)) may be aggregatedand made available over a single file system namespace (e.g.,/snapshots/). A directory for each virtual machine protected using thestorage appliance 300 may be created (e.g., the directory for VirtualMachine A may be/snapshots/VM_A). Snapshots and other data associatedwith a virtual machine may reside within the directory for the virtualmachine. In one example, snapshots of a virtual machine may be stored insubdirectories of the directory (e.g., a first snapshot of VirtualMachine A may reside in/snapshots/VM_A/s1/ and a second snapshot ofVirtual Machine A may reside in/snapshots/VM_A/s2/).

The distributed file system 312 may present itself as a single filesystem, in which. as new physical machines or nodes are added to thestorage appliance 300, the cluster may automatically discover theadditional nodes and automatically increase the available capacity ofthe file system for storing files and other data. Each file stored inthe distributed file system 312 may be partitioned into one or morechunks or shards. Each of the one or more chunks may be stored withinthe distributed file system 312 as a separate file. The files storedwithin the distributed file system 312 may be replicated or mirroredover a plurality of physical machines, thereby creating a load-balancedand fault tolerant distributed file system. In one example, storageappliance 300 may include ten physical machines arranged as a failovercluster and a first file corresponding with a snapshot of a virtualmachine (e.g., /snapshots/VM_A/s1/s1.full) may be replicated and storedon three of the ten machines.

The distributed metadata store 310 may include a distributed databasemanagement system that provides high availability without a single pointof failure. In one embodiment, the distributed metadata store 310 maycomprise a database, such as a distributed document-oriented database.The distributed metadata store 310 may be used as a distributed keyvalue storage system. In one example, the distributed metadata store 310may comprise a distributed NoSQL key value store database. In somecases, the distributed metadata store 310 may include a partitioned rowstore, in which rows are organized into tables or other collections ofrelated data held within a structured format within the key value storedatabase. A table (or a set of tables) may be used to store metadatainformation associated with one or more files stored within thedistributed file system 312. The metadata information may include thename of a file, a size of the file, file permissions associated with thefile, when the file was last modified, and file mapping informationassociated with an identification of the location of the file storedwithin a cluster of physical machines. In one embodiment, a new filecorresponding with a snapshot of a virtual machine may be stored withinthe distributed file system 312 and metadata associated with the newfile may be stored within the distributed metadata store 310. Thedistributed metadata store 310 may also be used to store a backupschedule for the virtual machine and a list of snapshots for the virtualmachine that are stored using the storage appliance 300.

In some cases, the distributed metadata store 310 may be used to manageone or more versions of a virtual machine. Each version of the virtualmachine may correspond with a full image snapshot of the virtual machinestored within the distributed file system 312 or an incremental snapshotof the virtual machine (e.g., a forward incremental or reverseincremental) stored within the distributed file system 312. In oneembodiment, the one or more versions of the virtual machine maycorrespond with a plurality of files. The plurality of files may includea single full image snapshot of the virtual machine and one or moreincremental aspects derived from the single full image snapshot. Thesingle full image snapshot of the virtual machine may be stored using afirst storage device of a first type (e.g., a HDD) and the one or moreincremental aspects derived from the single full image snapshot may bestored using a second storage device of a second type (e.g., an SSD). Inthis case, only a single full image needs to be stored, and each versionof the virtual machine may be generated from the single full image orthe single full image combined with a subset of the one or moreincremental aspects. Furthermore, each version of the virtual machinemay be generated by performing a sequential read from the first storagedevice (e.g., reading a single file from a HDD) to acquire the fullimage and, in parallel, performing one or more reads from the secondstorage device (e.g., performing fast random reads from an SSD) toacquire the one or more incremental aspects.

The distributed job scheduler 308 may be used for scheduling backup jobsthat acquire and store virtual machine snapshots for one or more virtualmachines over time. The distributed job scheduler 308 may follow abackup schedule to back up an entire image of a virtual machine at aparticular point in time or one or more virtual disks associated withthe virtual machine at the particular point in time. In one example, thebackup schedule may specify that the virtual machine be backed up at asnapshot capture frequency, such as every two hours or every 24 hours.Each backup job may be associated with one or more tasks to be performedin a sequence. Each of the one or more tasks associated with a job maybe run on a particular node within a cluster. In some cases, thedistributed job scheduler 308 may schedule a specific job to be run on aparticular node based on data stored on the particular node. Forexample, the distributed job scheduler 308 may schedule a virtualmachine snapshot job to be run on a node in a cluster that is used tostore snapshots of the virtual machine in order to reduce networkcongestion.

The distributed job scheduler 308 may comprise a distributed faulttolerant job scheduler, in which jobs affected by node failures arerecovered and rescheduled to be run on available nodes. In oneembodiment, the distributed job scheduler 308 may be fully decentralizedand implemented without the existence of a master node. The distributedjob scheduler 308 may run job scheduling processes on each node in acluster or on a plurality of nodes in the cluster. In one example, thedistributed job scheduler 308 may run a first set of job schedulingprocesses on a first node in the cluster, a second set of job schedulingprocesses on a second node in the cluster, and a third set of jobscheduling processes on a third node in the cluster. The first set ofjob scheduling processes, the second set of job scheduling processes,and the third set of job scheduling processes may store informationregarding jobs, schedules, and the states of jobs using a metadatastore, such as distributed metadata store 310. In the event that thefirst node running the first set of job scheduling processes fails(e.g., due to a network failure or a physical machine failure), thestates of the jobs managed by the first set of job scheduling processesmay fail to be updated within a threshold period of time (e.g., a jobmay fail to be completed within 30 seconds or within minutes from beingstarted). In response to detecting jobs that have failed to be updatedwithin the threshold period of time, the distributed job scheduler 308may undo and restart the failed jobs on available nodes within thecluster.

The job scheduling processes running on at least a plurality of nodes ina cluster (e.g., on each available node in the cluster) may manage thescheduling and execution of a plurality of jobs. The job schedulingprocesses may include run processes for running jobs, cleanup processesfor cleaning up failed tasks, and rollback processes for rolling-back orundoing any actions or tasks performed by failed jobs. In oneembodiment, the job scheduling processes may detect that a particulartask for a particular job has failed and, in response, may perform acleanup process to clean up or remove the effects of the particular taskand then perform a rollback process that processes one or more completedtasks for the particular job in reverse order to undo the effects of theone or more completed tasks. Once the particular job with the failedtask has been undone, the job scheduling processes may restart theparticular job on an available node in the cluster.

The distributed job scheduler 308 may manage a job in which a series oftasks associated with the job are to be performed atomically (i.e.,partial execution of the series of tasks is not permitted). If theseries of tasks cannot be completely executed or there is any failurethat occurs to one of the series of tasks during execution (e.g., a harddisk associated with a physical machine fails or a network connection tothe physical machine fails), then the state of a data management systemmay be returned to a state as if none of the series of tasks was everperformed. The series of tasks may correspond with an ordering of tasksfor the series of tasks and the distributed job scheduler 308 may ensurethat each task of the series of tasks is executed based on the orderingof tasks. Tasks that do not have dependencies with each other may beexecuted in parallel.

In some cases, the distributed job scheduler 308 may schedule each taskof a series of tasks to be performed on a specific node in a cluster. Inother cases, the distributed job scheduler 308 may schedule a first taskof the series of tasks to be performed on a first node in a cluster anda second task of the series of tasks to be performed on a second node inthe cluster. In these cases, the first task may have to operate on afirst set of data (e.g., a first file stored in a file system) stored onthe first node and the second task may have to operate on a second setof data (e.g., metadata related to the first file that is stored in adatabase) stored on the second node. In some embodiments, one or moretasks associated with a job may have an affinity to a specific node in acluster.

In one example, if the one or more tasks require access to a databasethat has been replicated on three nodes in a cluster, then the one ormore tasks may be executed on one of the three nodes. In anotherexample, if the one or more tasks require access to multiple chunks ofdata associated with a virtual disk that has been replicated over fournodes in a cluster, then the one or more tasks may be executed on one ofthe four nodes. Thus, the distributed job scheduler 308 may assign oneor more tasks associated with a job to be executed on a particular nodein a cluster based on the location of data that may be required to beaccessed by the one or more tasks.

In one embodiment, the distributed job scheduler 308 may manage a firstjob associated with capturing and storing a snapshot of a virtualmachine periodically (e.g., every 30 minutes). The first job may includeone or more tasks, such as communicating with a virtualizedinfrastructure manager, such as the virtualized infrastructure manager222 in FIG. 2, to create a frozen copy of the virtual machine and totransfer one or more chunks (or one or more files) associated with thefrozen copy to a storage appliance, such as storage appliance 300 inFIG. 1. The one or more tasks may also include generating metadata forthe one or more chunks, storing the metadata using the distributedmetadata store 310, storing the one or more chunks within thedistributed file system 312, and communicating with the virtualizedinfrastructure manager 222 that the frozen copy of the virtual machinemay be unfrozen or released from a frozen state. The metadata for afirst chunk of the one or more chunks may include information specifyinga version of the virtual machine associated with the frozen copy, a timeassociated with the version (e.g., the snapshot of the virtual machinewas taken at 5:30 p.m. on Jun. 29, 2018), and a file path to where thefirst chunk is stored within the distributed file system 312 (e.g., thefirst chunk is located at/snapshotsNM_B/s1/s1.chunk1). The one or moretasks may also include deduplication, compression (e.g., using alossless data compression algorithm such as LZ4 or LZ77), decompression,encryption (e.g., using a symmetric key algorithm such as Triple DES orAES-256), and decryption related tasks.

The virtualization interface 304 may provide an interface forcommunicating with a virtualized infrastructure manager managing avirtualization infrastructure, such as virtualized infrastructuremanager 222 in FIG. 2, and requesting data associated with virtualmachine snapshots from the virtualization infrastructure. Thevirtualization interface 304 may communicate with the virtualizedinfrastructure manager using an Application Programming Interface (API)for accessing the virtualized infrastructure manager (e.g., tocommunicate a request for a snapshot of a virtual machine). In thiscase, storage appliance 300 may request and receive data from avirtualized infrastructure without requiring agent software to beinstalled or running on virtual machines within the virtualizedinfrastructure. The virtualization interface 304 may request dataassociated with virtual blocks stored on a virtual disk of the virtualmachine that have changed since a last snapshot of the virtual machinewas taken or since a specified prior point in time. Therefore, in somecases, if a snapshot of a virtual machine is the first snapshot taken ofthe virtual machine, then a full image of the virtual machine may betransferred to the storage appliance. However, if the snapshot of thevirtual machine is not the first snapshot taken of the virtual machine,then only the data blocks of the virtual machine that have changed sincea prior snapshot was taken may be transferred to the storage appliance.

The virtual machine search index 306 may include a list of files thathave been stored using a virtual machine and a version history for eachof the files in the list. Each version of a file may be mapped to theearliest point-in-time snapshot of the virtual machine that includes theversion of the file or to a snapshot of the virtual machine thatincludes the version of the file (e.g., the latest point in timesnapshot of the virtual machine that includes the version of the file).In one example, the virtual machine search index 306 may be used toidentify a version of the virtual machine that includes a particularversion of a file (e.g., a particular version of a database, aspreadsheet, or a word processing document). In some cases, each of thevirtual machines that are backed up or protected using storage appliance300 may have a corresponding virtual machine search index.

In one embodiment, as each snapshot of a virtual machine is ingested,each virtual disk associated with the virtual machine is parsed in orderto identify a file system type associated with the virtual disk and toextract metadata (e.g., file system metadata) for each file stored onthe virtual disk. The metadata may include information for locating andretrieving each file from the virtual disk. The metadata may alsoinclude a name of a file, the size of the file, the last time at whichthe file was modified, and a content checksum for the file. Each filethat has been added, deleted, or modified since a previous snapshot wascaptured may be determined using the metadata (e.g., by comparing thetime at which a file was last modified with a time associated with theprevious snapshot). Thus, for every file that has existed within any ofthe snapshots of the virtual machine, a virtual machine search index maybe used to identify when the file was first created (e.g., correspondingwith a first version of the file) and at what times the file wasmodified (e.g., corresponding with subsequent versions of the file).Each version of the file may be mapped to a particular version of thevirtual machine that stores that version of the file.

In some cases, if a virtual machine includes a plurality of virtualdisks, then a virtual machine search index may be generated for eachvirtual disk of the plurality of virtual disks. For example, a firstvirtual machine search index may catalog and map files located on afirst virtual disk of the plurality of virtual disks and a secondvirtual machine search index may catalog and map files located on asecond virtual disk of the plurality of virtual disks. In this case, aglobal file catalog or a global virtual machine search index for thevirtual machine may include the first virtual machine search index andthe second virtual machine search index. A global file catalog may bestored for each virtual machine backed up by a storage appliance withina file system, such as distributed file system 312 in FIG. 3.

The data management system 302 may comprise an application running onthe storage appliance 300 that manages and stores one or more snapshotsof a virtual machine. In one example, the data management system 302 maycomprise a highest-level layer in an integrated software stack runningon the storage appliance. The integrated software stack may include thedata management system 302, the virtualization interface 304, thedistributed job scheduler 308, the distributed metadata store 310, andthe distributed file system 312.

In some cases, the integrated software stack may run on other computingdevices, such as a server or computing device 108 in FIG. 1. The datamanagement system 302 may use the virtualization interface 304, thedistributed job scheduler 308, the distributed metadata store 310, andthe distributed file system 312 to manage and store one or moresnapshots of a virtual machine. Each snapshot of the virtual machine maycorrespond with a point-in-time version of the virtual machine. The datamanagement system 302 may generate and manage a list of versions for thevirtual machine. Each version of the virtual machine may map to orreference one or more chunks and/or one or more files stored within thedistributed file system 312. Combined together, the one or more chunksand/or the one or more files stored within the distributed file system312 may comprise a full image of the version of the virtual machine.

FIG. 4 shows an example cluster 400 of a distributed decentralizeddatabase, according to some example embodiments. As illustrated, theexample cluster 400 includes five nodes, nodes 1-5. In some exampleembodiments, each of the five nodes runs from different machines, suchas physical machine 314 in FIG. 3 or virtual machine 220 in FIG. 2. Thenodes in the example cluster 400 can include instances of peer nodes ofa distributed database (e.g., cluster-based database, distributeddecentralized database management system, a NoSQL database, ApacheCassandra, DataStax, MongoDB, CouchDB), according to some exampleembodiments. The distributed database system is distributed in data issharded or distributed across the example cluster 400 in shards orchunks and decentralized in that there is no central storage device andno single point of failure. The system operates under an assumption thatmultiple nodes may go down, up, or become non-responsive, and so on.Sharding is splitting up of the data horizontally and managing eachshard separately on different nodes. For example, if the data managed bythe example cluster 400 can be indexed using the 26 letters of thealphabet, node 1 can manage a first shard that handles records thatstart with A through E, node 2 can manage a second shard that handlesrecords that start with F through J, and so on.

In some example embodiments, data written to one of the nodes isreplicated to one or more other nodes per a replication protocol of theexample cluster 400. For example, data written to node 1 can bereplicated to nodes 2 and 3. If node 1 prematurely terminates, node 2and/or 3 can be used to provide the replicated data. In some exampleembodiments, each node of example cluster 400 frequently exchanges stateinformation about itself and other nodes across the example cluster 400using gossip protocol. Gossip protocol is a peer-to-peer communicationprotocol in which each node randomly shares (e.g., communicates,requests, transmits) location and state information about the othernodes in a given cluster.

Writing: For a given node, a sequentially written commit log capturesthe write activity to ensure data durability. The data is then writtento an in-memory structure (e.g., a memtable, write-back cache). Eachtime the in-memory structure is full, the data is written to disk in aSorted String Table data file. In some example embodiments, writes areautomatically partitioned and replicated throughout the example cluster400.

Reading: Any node of example cluster 400 can receive a read request(e.g., query) from an external client. If the node that receives theread request manages the data requested, the node provides the requesteddata. If the node does not manage the data, the node determines whichnode manages the requested data. The node that received the read requestthen acts as a proxy between the requesting entity and the node thatmanages the data (e.g., the node that manages the data sends the data tothe proxy node, which then provides the data to an external entity thatgenerated the request).

The distributed decentralized database system is decentralized in thatthere is no single point of failure due to the nodes being symmetricaland seamlessly replaceable. For example, whereas conventionaldistributed data implementations have nodes with different functions(e.g., master/slave nodes, asymmetrical database nodes, federateddatabases), the nodes of example cluster 400 are configured to functionthe same way (e.g., as symmetrical peer database nodes that communicatevia gossip protocol, such as Cassandra nodes) with no single point offailure. If one of the nodes in example cluster 400 terminatesprematurely (“goes down”), another node can rapidly take the place ofthe terminated node without disrupting service. The example cluster 400can be a container for a keyspace, which is a container for data in thedistributed decentralized database system (e.g., whereas a database is acontainer for containers in conventional relational databases, theCassandra keyspace is a container for a Cassandra database system).

In some examples, a data management platform or backup service archivesa snapshot in the cloud (e.g. S3, Azure) for long-term retention. Eachsnapshot is stored as a sparse file known as patch file. When a userwants to restore the snapshot from cloud to a local server, aconsolidated patch file is created locally by reading pieces fromvarious patch files in the cloud. Each patch file in the cloudrepresents one snapshot. The logical view of the snapshot to bedownloaded is given by the entire chain of patch files across allsnapshots leading up to the to-be-downloaded snapshot. One challenge isthat reading small segments of data from various patch files from thecloud is slow. Cloud reads have high latency. Most commerciallyavailable cloud services offer better overall throughput if larger readsare issued. In addition, some cloud services charge clients on thenumber of reads issued. Hence, being able to read in larger chunks givesbetter throughput at lower cost. Further, most cloud providers have theproperty that maximum throughput is achieved if multiple reads areissued in parallel.

In this disclosure, some examples employ a two-phase approach todownload a snapshot from the cloud that seeks to achieve maximumthroughput with minimum cost by performing large reads from the cloud inparallel. To this end, in a first phase (also known as a “dry-run”phase), some examples build a profile that defines precisely which partsof which source patch file (described further below) should be read, andin what specific order, for a “most effective” download. In a secondphase (also known as a data-transfer phase), some examples use thisprofile to coalesce smaller reads into larger ones and read them fromthe cloud in parallel. During the second phase, some examples know aheadof time exactly what objects to read which helps a quick read limited toexactly what data is needed for a given snapshot download. That is, someexamples do not read any data that would only be discarded later,thereby reducing cost and improving RPOs for system administrators andusers.

Some example systems and methods use a sparse representation to storedata for a snapshot. This sparse representation may be called a patchfile. This file only stores data blocks (typically 64 KB in size) thathave changed since a previous snapshot. A logical view of a snapshot isgiven by all the patch files, in order, from all prior snapshots up tothe snapshot in question. FIG. 5 shows how an example patch file 502 islaid out on a disk. Logically, the patch file 502 is a key-value storewhere the key is the logical offset of the data and the value is a 64 KBdata block (except for the very last block 510 which can be smaller). Apatch file for a snapshot only contains keys (logical offsets) for datablocks that have changed since the last snapshot was taken. This is whywe may have “logical holes” 504 as shown in FIG. 5. Some examplesinclude a special index block 506 for a group of data blocks 508, forexample. These index blocks 506 are very small compared to the datablocks 508 (approximately a 200 KB index block can index 1 GB of actualdata blocks) and allows some example embodiments to locate data blocks508 quickly without scanning the entire file (that is, these indexblocks 506 facilitate key-value lookup scheme). An index block 506representing a set of data blocks 508 can be placed either before thedata blocks 508 or after the data blocks 508 (depending onimplementation).

In some examples, each snapshot stored in the cloud is represented byone patch file. When a user wants to download (i.e. recover) aparticular snapshot, examples review (identify) all patch files up toand including the to-be-downloaded snapshot and download relevant blocksfrom the cloud in order to create a local patch file which represents aconsolidated view of all those cloud patch files.

FIG. 6 shows an example download mechanism 602 in which an example patchfile 604 (represented by Patch2) is downloaded from the cloud to a localserver. In some embodiments, the download mechanism 602 may be asoftware-level component of a storage appliance in a data center in anetworked computing environment, such as the networked computingenvironment 100 in FIG. 1. In some embodiments, the download mechanism602 may be integrated into a data management system for managing virtualmachines and data backups in a storage appliance, for example a storageappliance described above.

Referring again to FIG. 6, Patch2 is dependent on two previous snapshots606 and 608 in the cloud (Patch1 and Patch0). For a local recovery, alocal patch file is downloaded locally. In some examples, the localpatch file includes a consolidated version of the three patch files 604,606, and 608. For a particular logical offset, the same data block 610can exist in more than one patch file. In such cases, some examplesdownload the relevant block (here data block 610) from the newest patchfile. In FIG. 6, all blocks to be downloaded locally are colored whiteand the blocks that are to be discarded (since a block at the sameoffset exists in a newer patch) file are colored gray. The blocks 610are colored accordingly, as an example.

Some example embodiments iterate the index blocks of all patch filesfrom start to finish in the order of logical offset shown by the arrow612 in FIG. 6. The index blocks 616 contain information that indicateswhich data blocks (form example, data block 610) should be downloadedand which data blocks can be discarded. If a data block is identifiedfor downloading, it is downloaded and added to the local patch file atthe same logical offset.

In some example embodiments, this approach may have some drawbacks. Forexample, these embodiments can invoke multiple 64 KB reads at a time.Cloud reads have high latency and smaller reads suffer more. Sometesting has revealed for example that reads of size 8 MB or more providethe best latency and throughput results. This may vary to some degreebased on vendor and connection, but 64 KB reads are too small. Indeed,in some instances, smaller reads are not only slower but they are alsocostlier (most cloud vendors charge per read request). Another drawbackrelates to issuing 64 KB reads serially one at a time. To maximizethroughput, most cloud vendors require parallel issuance of reads.

One approach for addressing these challenges includes issuing largerreads for the next sequential offsets of a particular file and hope thatthe next data blocks needed from this file can be satisfied from cachedreads. This approach can be augmented with parallel prefetching offuture sequential offsets to achieve parallelism. The problem with theseapproaches is that they may still cause downloading of redundant datafrom the cloud that will eventually be discarded, incurring higher costsfor users. Moreover, it is possible that these approaches do notdownload the correct or required data blocks thus hindering or evenpreventing maximum throughout. A problem remains in that the correct,future data blocks that need to be downloaded for a full (consolidated)local patch file are not identified ahead of time.

For example, returning to FIG. 6, if we look at Patch1 (file 606) in thecloud, most of its data blocks are to be discarded (colored gray asredundant), hence they should not be downloaded. As the Patch1 file 606is scanned from left to right to perform cloud reads, the potentialsolutions do not know whether the future data blocks (i.e. further tothe right) will be relevant or redundant until they have finishedscanning the entire file.

In sum, a significant problem presented by conventional approaches isthat as the next 64 KB data block is read from a particular file in thecloud, it is still not known which future offsets from the same filewill be read. A potential solution includes reading only 64 KB blocks(small reads) but this suffers from the drawbacks discussed above, whileanother potential solution prevents issuing future reads asynchronouslyin parallel ahead in time to achieve better throughput.

Some present example embodiments of this disclosure address these andother challenges by including two-phase approach for cloud reads and, insome examples, provide systems and methods for two-phase snapshotrecovery.

Some embodiments include a Phase-1 in a two-phase method of snapshotrecovery. This phase may also be called a dry-run phase (for examplephase 802 in FIG. 8, described in more detail below). In this phase,some embodiments scan the index blocks of the cloud patch files andidentify which data blocks from which cloud files need to be read tocreate a local consolidated patch file (for example, 806 in FIG. 8).Instead of reading the data blocks themselves, this block identificationinformation is stored in a new file format called a “patch file image”.An example of a patch file image 702 is shown in FIG. 7. Other examplesare possible.

Some embodiments include a Phase-2 in a two-phase method of snapshotrecovery. This phase may also be called a data-transfer phase (forexample phase 804 in FIG. 8, described in more detail below). In thisphase, some embodiments scan the patch file image previously created inthe dry-run phase from start to finish and issue larger reads inparallel ahead of time to address the cloud reads and redundancyproblems discussed above. Examples of a two-phase approach seek toachieve maximum data throughput possible without causing a downloadingof redundant data. In adopting a two-phase approach, such examples arepre-informed or instructed by a full set of future reads for downloadingand creating a complete local patch file based on the patch file imagebuilt in the previous dry-run phase.

With reference to the example patch file image 702 of FIG. 7, in someaspects this image or format is similar to the real patch file that itrepresents, except that, in some examples, instead of storing actual 64KB data blocks, each block of the patch file image 702 stores a tuple704. An example tuple 704 includes values for or information concerninga cloud patch file path, a cloud patch file offset, and a cloud patchfile size identifying which cloud patch file and which location withinthe file the real data should be downloaded from.

In some examples, the file size of the patch file image is very smallcompared to the actual patch file since it does not store actual data.For example, a patch file image of a patch file in the range of 200-300GB in size may be in the range 30-40 MB, and in one example the patchfile is 230 GB and the patch file image is 37 MB. This enables storageof the intermediate patch file images file in fast media (e.g. SSD) tominimize or even avoid the cost of more expensive slow media (HDD)writes and reads. It should be noted that building a patch file imageinvolves reading index blocks from the cloud patch files but avoidsreading data blocks from cloud patch files. Index blocks are very smallcompared to the actual data blocks (for example, one index block of 200KB covers up to 1 GB of read data).

With reference to FIG. 8, in a data-transfer phase 804, some embodimentsscan a patch file image 702 from start to finish, read the data blocks(for example, data block 610 and others) in larger chunks and inparallel to maximize throughput and minimize latency. Exampleembodiments perform smart coalescing using the previously obtainedinformation of future reads (derived from the patch file image) so thatlarger chunks from the same file are read in parallel. Coalescing hererefers to a mechanism whereby contiguous blocks of data from the samepatch file in the cloud are merged into larger reads. If data is sofragmented across patch files that coalescing fails to create largerchunks, some embodiments can still leverage parallel future reads with alarge number of threads to maximize throughput. In some embodiments,this can all be done without downloading any redundant data from thecloud. In some examples, the coalescing mechanism permits a certaindegree of data wastage if that results in overall larger read sizes onaverage (this is configurable). In some examples, the two-phase approachdisclosed herein enables saturation of the available read bandwidth tothe cloud (for example in S3, Azure or others).

With reference to FIGS. 9-10, certain operations in a method oftwo-phase snapshot recovery are shown for a data management and storage(DMS) platform accessing a cluster comprising peer DMS nodes and adistributed data store comprising local and cloud storage. Withreference to FIG. 9, a method 900 of creating a local consolidated patchfile from a patch file chain stored in the cloud storage, comprises: inoperation 902, in a first dry-run phase, creating a patch file image ofdata blocks in one or more cloud patch files stored in the cloudstorage. With reference to FIG. 10, the method 900 may further comprise,at operation 904, in a second data-transfer phase, downloading at leastsome of the data blocks from the cloud patch files identified by thepatch file image; and, in operation 906, creating and storing, in thelocal storage, the local consolidated patch file using the downloadeddata blocks.

In some examples, the method 900 further comprises performing a snapshotrecovery using the local consolidated patch file.

In some examples, the first dry-run phase further comprises scanningindex blocks in the one or more cloud patch files to identify datablocks for downloading in the second data-transfer phase, the patch fileimage based on the scanning of the index blocks.

In some examples, the patch file image includes one or more tuples, eachtuple including values for or information concerning a cloud patch filepath, a cloud patch file offset, and a cloud patch file size.

In some examples, the second data-transfer phase further comprisesscanning the patch image file created in the first dry-run phase anddownloading at least some of the data blocks identified for downloadingsimultaneously.

In some examples, the second data-transfer phase comprise a coalescingoperation to construct larger reads than would occur when read inparallel.

In some examples, a file size of the cloud patch file is in the range200-300 GB, and a file size of the patch file image is in the range30-40 MB.

FIG. 11 is a block diagram illustrating an example of a computersoftware architecture for data classification and information securitythat may be installed on a machine, according to some exampleembodiments. FIG. 11 is merely a non-limiting example of a softwarearchitecture 1102, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 1102 may be executing onhardware such as a machine 1300 of FIG. 13 that includes, among otherthings, processor 1246, memory 1248, and I/O components 1250. Arepresentative hardware layer 1104 of FIG. 11 is illustrated and canrepresent, for example, the machine 1300 of FIG. 13. The representativehardware layer 1104 of FIG. 11 comprises one or more processing units1106 having associated executable instructions 1108. The executableinstructions 1108 represent the executable instructions of the softwarearchitecture 1102, including implementation of the methods, modules, andso forth described herein. The representative hardware layer 1104 alsoincludes memory or storage modules 1110, which also have the executableinstructions 1108. The representative hardware layer 1104 may alsocomprise other hardware 1112, which represents any other hardware of therepresentative hardware layer 1104, such as the other hardwareillustrated as part of the machine 1100.

In the example architecture of FIG. 11, the software architecture 1102may be conceptualized as a stack of layers, where each layer providesparticular functionality. For example, the software architecture 1102may include layers such as an operating system 1114, libraries 1118,frameworks/middleware 1116, applications 1120, and a presentation layer1142. Operationally, the applications 1120 or other components withinthe layers may invoke API calls 1122 through the software stack andreceive a response, returned values, and so forth (illustrated asmessages 1124) in response to the API calls 1122. The layers illustratedare representative in nature, and not all software architectures haveall layers. For example, some mobile or special purpose operatingsystems may not provide a frameworks/middleware 1116 layer, while othersmay provide such a layer. Other software architectures may includeadditional or different layers.

The operating system 1114 may manage hardware resources and providecommon services. The operating system 1114 may include, for example, akernel 1128, services 1126, and drivers 1130. The kernel 1128 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1128 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1126 may provideother common services for the other software layers. The drivers 1130may be responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1130 may include display drivers,camera drivers, Bluetooth® drivers, flash memory drivers, serialcommunication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi®drivers, audio drivers, power management drivers, and so forth dependingon the hardware configuration.

The libraries 1118 may provide a common infrastructure that may beutilized by the applications 1120 and/or other components and/or layers.The libraries 1118 typically provide functionality that allows othersoftware modules to perform tasks in an easier fashion than byinterfacing directly with the underlying operating system 1114functionality (e.g., kernel 1128, services 1126, or drivers 1130). Thelibraries 1118 may include system libraries 1132 (e.g., C standardlibrary) that may provide functions such as memory allocation functions,string manipulation functions, mathematic functions, and the like. Inaddition, the libraries 1118 may include API libraries 1134 such asmedia libraries (e.g., libraries to support presentation andmanipulation of various media formats such as MPEG4, H.264, MP3, AAC,AMR, JPG, PNG), graphics libraries (e.g., an OpenGL framework that maybe used to render 2D and 3D graphic content on a display), databaselibraries (e.g., SQLite that may provide various relational databasefunctions), web libraries (e.g., WebKit that may provide web browsingfunctionality), and the like. The libraries 1118 may also include a widevariety of other libraries 1136 to provide many other APIs to theapplications 1120 and other software components/modules.

The frameworks 1116 (also sometimes referred to as middleware) mayprovide a higher-level common infrastructure that may be utilized by theapplications 1120 or other software components/modules. For example, theframeworks 1116 may provide various graphic user interface (GUI)functions, high-level resource management, high-level location services,and so forth. The frameworks 1116 may provide a broad spectrum of otherAPIs that may be utilized by the applications 1120 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system or platform.

The applications 1120 include built-in applications 1138 and/orthird-party applications 1140. Examples of representative built-inapplications 1138 may include, but are not limited to, a homeapplication, a contacts application, a browser application, a bookreader application, a location application, a media application, amessaging application, or a game application.

The third-party applications 1140 may include any of the built-inapplications 1138, as well as a broad assortment of other applications.In a specific example, the third-party applications 1140 (e.g., anapplication developed using the Android™ or iOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform)may be mobile software running on a mobile operating system such asiOS™, Android™, Windows® Phone, or other mobile operating systems. Inthis example, the third-party applications 1140 may invoke the API calls1122 provided by the mobile operating system such as the operatingsystem 1114 to facilitate functionality described herein.

The applications 1120 may utilize built-in operating system functions(e.g., kernel 1128, services 1126, or drivers 1130), libraries (e.g.,system libraries 1132, API libraries 1134, and other libraries 1136), orframeworks/middleware 1116 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such asthe presentation layer 1142. In these systems, the application/module“logic” can be separated from the aspects of the application/module thatinteract with the user.

Some software architectures utilize virtual machines. In the example ofFIG. 11, this is illustrated by a virtual machine 1146. A virtualmachine creates a software environment where applications/modules canexecute as if they were executing on a hardware machine e.g., themachine 1300 of FIG. 13, for example). A virtual machine 1146 is hostedby a host operating system (e.g., operating system 1114) and typically,although not always, has a virtual machine monitor 1144, which managesthe operation of the virtual machine 1146 as well as the interface withthe host operating system (e.g., operating system 1114). A softwarearchitecture executes within the virtual machine 1146, such as anoperating system 1148, libraries 1156, frameworks/middleware 1154,applications 1152, or a presentation layer 1150. These layers ofsoftware architecture executing within the virtual machine 1146 can bethe same as corresponding layers previously described or may bedifferent.

FIG. 12 is a block diagram 1200 illustrating an architecture of software1202, which can be installed on any one or more of the devices describedabove. FIG. 12 is merely a non-limiting example of a softwarearchitecture, and it will be appreciated that many other architecturescan be implemented to facilitate the functionality described herein. Invarious embodiments, the software 1202 is implemented by hardware suchas a machine 1300 of FIG. 13 that includes processor(s) 1246, memory1248, and input/output (I/O) components 1250. In this examplearchitecture, the software 1202 can be conceptualized as a stack oflayers where each layer may provide a particular functionality. Forexample, the software 1202 includes layers such as an operating system1204, libraries 1208, frameworks 1206, and applications 1210.Operationally, the applications 1210 invoke API calls 1212 (applicationprogramming interface) through the software stack and receive messages1214 in response to the API calls 1212, consistent with someembodiments.

In various implementations, the operating system 1204 manages hardwareresources and provides common services. The operating system 1004includes, for example, a kernel 1216, services 1220, and drivers 1218.The kernel 1216 acts as an abstraction layer between the hardware andthe other software layers, consistent with some embodiments. Forexample, the kernel 1216 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionality. The services 1220 canprovide other common services for the other software layers. The drivers1218 are responsible for controlling or interfacing with the underlyinghardware, according to some embodiments. For instance, the drivers 1218can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH®Low Energy drivers, flash memory drivers, serial communication drivers(e.g., USB drivers), WI-FI® drivers, audio drivers, power managementdrivers, and so forth.

In some embodiments, the libraries 1208 provide a low-level commoninfrastructure utilized by the applications 1210. The libraries 1208 caninclude system libraries 1222 (e.g., C standard library) that canprovide functions such as memory allocation functions, stringmanipulation functions, mathematic functions, and the like. In addition,the libraries 1208 can include API libraries 1224 such as medialibraries (e.g., libraries to support presentation and manipulation ofvarious media formats such as MPEG4, H.264 or AVC, MP3, AAC, AMR audiocodec, JPEG or JPG, or PNG), graphics libraries (e.g., an OpenGLframework used to render in two dimensions (2D) and three dimensions(3D) in a graphic content on a display), database libraries (e.g.,SQLite to provide various relational database functions), web libraries(e.g., WebKit to provide web browsing functionality), and the like. Thelibraries 1208 can also include a wide variety of other libraries 1226to provide many other APIs to the applications 1210.

The frameworks 1206 provide a high-level common infrastructure that canbe utilized by the applications 1210, according to some embodiments. Forexample, the frameworks 1206 provide various GUI functions, high-levelresource management, high-level location services, and so forth. Theframeworks 1206 can provide a broad spectrum of other APIs that can beutilized by the applications 1210, some of which may be specific to aparticular operating system or platform.

In an example embodiment, the applications 1210 include a homeapplication 1228, a contacts application 1230, a browser application1232, a book reader application 1234, a location application 1236, amedia application 1238, a messaging application 1240, a game application1242, and a broad assortment of other applications, such as athird-party application 1244. According to some embodiments, theapplications 1210 are programs that execute functions defined in theprograms. Various programming languages can be employed to create one ormore of the applications 1210, structured in a variety of manners, suchas object-oriented programming languages (e.g., Objective-C, Java, orC++) or procedural programming languages (e.g., C or assembly language).In a specific example, the third-party application 1244 (e.g., anapplication developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform)may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. Inthis example, the third-party application 1244 can invoke the API calls1212 provided by the operating system 1204 to facilitate functionalitydescribed herein.

FIG. 13 illustrates a diagrammatic representation of a machine 1300 inthe form of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 13 shows a diagrammatic representation of the machine1300 in the example form of a computer system, within which instructions1306 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1300 to perform any oneor more of the methodologies discussed herein may be executed.Additionally, or alternatively, the instructions 1306 may implement theoperations of the methods summarized or described herein.

The instructions 1306 transform the general, non-programmed machine 1300into a particular machine 1300 programmed to carry out the described andillustrated functions in the manner described. In alternativeembodiments, the machine 1300 operates as a standalone device or may becoupled (e.g., networked) to other machines. In a networked deployment,the machine 1300 may operate in the capacity of a server machine or aclient machine in a server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine 1300 may comprise, but not be limited to, a server computer, aclient computer, a personal computer (PC), a tablet computer, a laptopcomputer, a netbook, a set-top box (STB), a PDA, an entertainment mediasystem, a cellular telephone, a smart phone, a mobile device, a wearabledevice (e.g., a smart watch), a smart home device (e.g., a smartappliance), other smart devices, a web appliance, a network router, anetwork switch, a network bridge, or any machine capable of executingthe instructions 1306, sequentially or otherwise, that specify actionsto be taken by the machine 1300. Further, while only a single machine1300 is illustrated, the term “machine” shall also be taken to include acollection of machines 1300 that individually or jointly execute theinstructions 1306 to perform any one or more of the methodologiesdiscussed herein.

The machine 1300 may include processor(s) 1246, memory 1248, and I/Ocomponents 1250, which may be configured to communicate with each othersuch as via a bus 1302. In an example embodiment, the processor(s) 1246(e.g., a CPU, a Reduced Instruction Set Computing (RISC) processor, aComplex Instruction Set Computing (CISC) processor, a GPU, a DigitalSignal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit(RFIC), another processor, or any suitable combination thereof) mayinclude, for example, a processor 1304 and a processor 1308 that mayexecute the instructions 1306. The term “processor” is intended toinclude multi-core processors that may comprise two or more independentprocessors (sometimes referred to as “cores”) that may executeinstructions contemporaneously. Although FIG. 13 shows multipleprocessor(s) 1246, the machine 1300 may include a single processor witha single core, a single processor with multiple cores (e.g., amulti-core processor), multiple processors with a single core, multipleprocessors with multiples cores, or any combination thereof.

The memory 1248 may include a main memory 1312, a static memory 1310,and a storage unit 1316, each accessible to the processor(s) 1246 suchas via the bus 1302. The main memory 1312, the static memory 1310, andstorage unit 1316 store the instructions 1306 embodying any one or moreof the methodologies or functions described herein. The instructions1306 may also reside, completely or partially, within the main memory1312, within the static memory 1310, within the storage unit 1316,within at least one of the processor(s) 1246 (e.g., within theprocessor's cache memory), or any suitable combination thereof, duringexecution thereof by the machine 1300.

The I/O components 1250 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1250 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components1250 may include many other components that are not shown in FIG. 13.The I/O components 1250 are grouped according to functionality merelyfor simplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the i/O components 1250 mayinclude output components 1320 and input components 1322. The outputcomponents 1320 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1322 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1250 may includebiometric components 1324, motion components 1326, environmentalcomponents 1328, or position components 1330, among a wide array ofother components. For example, the biometric components 1324 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram-basedidentification), and the like. The motion components 1326 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1328 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position components 1330 mayinclude location sensor components (e.g., a GPS receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1250 may include communication components 1334operable to couple the machine 1300 to a network 1338 or devices 1332via a coupling 1314 and a coupling 1336, respectively. For example, thecommunication components 1334 may include a network interface componentor another suitable device to interface with the network 1338. Infurther examples, the communication components 1334 may include wiredcommunication components, wireless communication components, cellularcommunication components, Near Field Communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 1332 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 1334 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1334 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1334, such as location via IP geolocation, location via Wi-Fi® signaltriangulation, location via detecting an NFC beacon signal that mayindicate a particular location, and so forth.

The various memories (i.e., memory 1248, main memory 1312, and/or staticmemory 1310) and/or storage unit 1316 may store one or more sets ofinstructions and data structures (e.g., software) embodying or utilizedby any one or more of the methodologies or functions described herein.These instructions (e.g., the instructions 1306), when executed byprocessor(s) 1246, cause various operations to implement the disclosedembodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” “computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. In some examples, a storage deviceor medium accepts or receives random writes The terms shall accordinglybe taken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia and/or device-storage media include non-volatile memory, includingby way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), FPGA, and flash memory devices;magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms“machine-storage media,” “computer-storage media,” and “device-storagemedia” specifically exclude carrier waves, modulated data signals, andother such media, at least some of which are covered under the term“signal medium” discussed below.

In various example embodiments, one or more portions of the network 1338may be an ad hoc network, an intranet, an extranet, a VPN, a LAN, aWLAN, a WAN, a WWAN, a MAN, the Internet, a portion of the Internet, aportion of the PSTN, a plain old telephone service (POTS) network, acellular telephone network, a wireless network, a Wi-Fi® network,another type of network, or a combination of two or more such networks.For example, the network 1338 or a portion of the network 1338 mayinclude a wireless or cellular network, and the coupling 1314 may be aCode Division Multiple Access (CDMA) connection, a Global System forMobile communications (GSM) connection, or another type of cellular orwireless coupling. In this example, the coupling 1314 may implement anyof a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long rangeprotocols, or other data transfer technology.

The instructions 1306 may be transmitted or received over the network1338 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1334) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions1306 may be transmitted or received using a transmission medium via thecoupling 1336 (e.g., a peer-to-peer coupling) to the devices 1332. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 1306 for execution by the machine 1300, and includesdigital or analog communications signals or other intangible media tofacilitate communication of such software. Hence, the terms“transmission medium” and “signal medium” shall be taken to include anyform of modulated data signal, carrier wave, and so forth. The term“modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a matter as to encode informationin the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals. In some examples, the storage devices/media accept or receivingrandom writes.

Although examples have been described with reference to specific exampleembodiments or methods, it will be evident that various modificationsand changes may be made to these embodiments without departing from thebroader scope of the embodiments. Accordingly, the specification anddrawings are to be regarded in an illustrative rather than a restrictivesense. The accompanying drawings that form a part hereof, show by way ofillustration, and not of limitation, specific embodiments in which thesubject matter may be practiced. The embodiments illustrated aredescribed in sufficient detail to enable those skilled in the art topractice the teachings disclosed herein. Other embodiments may beutilized and derived therefrom, such that structural and logicalsubstitutions and changes may be made without departing from the scopeof this disclosure. This detailed description, therefore, is not to betaken in a limiting sense, and the scope of various embodiments isdefined only by the appended claims, along with the full range ofequivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

What is claimed is:
 1. In a data management and storage (DMS) platformaccessing a cluster comprising peer DMS nodes and a distributed datastore comprising local and cloud storage, a method of creating a localconsolidated patch file from a patch file chain stored in the cloudstorage, the method comprising: in a first dry-run phase, creating apatch file image of data blocks in one or more cloud patch files storedin the cloud storage; in a second data-transfer phase, downloading atleast some of the data blocks from the cloud patch files identified bythe patch file image; and creating and storing, in the local storage,the local consolidated patch file using the downloaded data blocks. 2.The method of claim 1, further comprising performing a snapshot recoveryusing the local consolidated patch file.
 3. The method of claim 1,wherein the first dry-run phase further comprises scanning index blocksin the one or more cloud patch files to identify data blocks fordownloading in the second data-transfer phase, the patch file imagebased on the scanning of the index blocks.
 4. The method of claim 3,wherein the patch file image includes one or more tuples, each tupleincluding values for or information concerning a cloud patch file path,a cloud patch file offset, and a cloud patch file size.
 5. The method ofclaim 4, wherein the second data-transfer phase further comprisesscanning the patch image file created in the first dry-run phase anddownloading at least some of the data blocks identified for downloadingsimultaneously.
 6. The method of claim 5, wherein the seconddata-transfer phase comprise a coalescing operation to construct largerreads than would occur when read in parallel.
 7. The method of claim 1,wherein a file size of the cloud patch file is in the range 200-300 GB,and a file size of the patch file image is in the range 30-40 MB.
 8. Adata management and storage (DMS) platform, comprising: peer DMS nodesin a node cluster; a distributed data store comprising local and cloudstorage; and at least one processor configured to perform operations ina method of creating a local consolidated patch file from a patch filechain stored in the cloud storage, the operations comprising: in a firstdry-run phase, creating a patch file image of data blocks in one or morecloud patch files stored in the cloud storage; in a second data-transferphase, downloading at least some of the data blocks from the cloud patchfiles identified by the patch file image; and creating and storing, inthe local storage, the local consolidated patch file using thedownloaded data blocks.
 9. The DMS platform of claim 8, wherein theoperations further comprise performing a snapshot recovery using thelocal consolidated patch file.
 10. The DMS platform of method of claim8, wherein the first dry-run phase further comprises scanning indexblocks in the one or more cloud patch files to identify data blocks fordownloading in the second data-transfer phase, the patch file imagebased on the scanning of the index blocks.
 11. The DMS platform of claim10, wherein the patch file image includes one or more tuples, each tupleincluding values for or information concerning a cloud patch file path,a cloud patch file offset, and a cloud patch file size.
 12. The DMSplatform of claim 11, wherein the second data-transfer phase furthercomprises scanning the patch image file created in the first dry-runphase and downloading at least some of the data blocks identified fordownloading simultaneously.
 13. The DMS platform of claim 12, whereinthe second data-transfer phase comprise a coalescing operation toconstruct larger reads than would occur when read in parallel.
 14. TheDMS platform of claim 8, wherein a file size of the cloud patch file isin the range 200-300 GB, and a file size of the patch file image is inthe range 30-40 MB.
 15. A non-transitory machine-readable mediumcomprising instructions which, when read by a machine, cause the machineto implement operations in a method of creating a local consolidatedpatch file from a patch file chain stored in a cloud storage, theoperations comprising: in a first dry-run phase, creating a patch fileimage of data blocks in one or more cloud patch files stored in thecloud storage; in a second data-transfer phase, downloading at leastsome of the data blocks from the cloud patch files identified by thepatch file image; and creating and storing, in a local storage, thelocal consolidated patch file using the downloaded data blocks.
 16. Themedium of claim 15, wherein the operations further comprise performing asnapshot recovery using the local consolidated patch file.
 17. Themedium of claim 16, wherein the first dry-run phase further comprisesscanning index blocks in the one or more cloud patch files to identifydata blocks for downloading in the second data-transfer phase, the patchfile image based on the scanning of the index blocks.
 18. The medium ofclaim 17, wherein the patch file image includes one or more tuples, eachtuple including values for or information concerning a cloud patch filepath, a cloud patch file offset, and a cloud patch file size.
 19. Themedium of claim 18, wherein the second data-transfer phase furthercomprises scanning the patch image file created in the first dry-runphase and downloading at least some of the data blocks identified fordownloading simultaneously.
 20. The medium of claim 19, wherein thesecond data-transfer phase comprise a coalescing operation to constructlarger reads than would occur when read in parallel.